Method and system for vehicular guidance with respect to harvested crop

ABSTRACT

A discriminator identifies windrow pixels associated with a windrow within a collected image. A definer defines a search space with respect to a vehicle. An evaluator determines respective spatial correlations between the defined search space and the windrow pixels for different angular displacements of the search space. An alignment detector or search engine determining a desired vehicular heading as a preferential angular displacement associated with a generally maximum spatial correlation between the defined search space and the windrow pixels. An offset calculator estimates an offset of the vehicle to a central point of the windrow or a depth axis to achieve the desired vehicle heading and desired position of the vehicle with respect to the windrow.

This application is a divisional of application Ser. No. 11/342,760,filed Jan. 30, 2006 with a status of Allowed.

This document (including all drawings) claims priority based on U.S.provisional application Ser. No. 60/696,364, filed Jul. 1, 2005, andentitled, METHOD AND SYSTEM FOR VEHICULAR GUIDANCE USING A CROP IMAGEunder 35 U.S.C. 119(e).

FIELD OF THE INVENTION

This invention relates to a method and system for vehicular guidancewith respect to windrows or harvested crop lying in a field or on theground.

BACKGROUND OF THE INVENTION

A vision system may attempt to infer the relative position of a vehiclewith respect to harvested crop (e.g., hay) lying on the ground in afield. However, background art vision systems may require excessivecomputational resources or tend to respond too slowly for real-timenavigation of a vehicle. Further, vision systems may be inaccuratebecause of variations or discontinuities in the windrows, harvested croprows, or generally rectangular piles of clippings lying in the field.Therefore, a need exists for a robust vision system for vehicle guidancethat is less computationally demanding, more responsive, and moreresistant to guidance errors associated with variations in the windrows,harvested crop rows, or generally rectangular piles of clippings lyingin the field.

SUMMARY OF THE INVENTION

A method and system of guiding a vehicle comprises a discriminator foridentifying windrow pixels associated with a windrow within a collectedimage. A definer defines a search space with respect to a vehicle, wherethe search space contains a series of scan line segments. An evaluatordetermines respective spatial correlations between the defined searchspace and the windrow pixels for different angular displacements of thesearch space. An alignment detector or search engine determines adesired vehicular heading as a preferential angular displacementassociated with a generally maximum spatial correlation between thedefined search space and the windrow pixels. A central point isestimated, where the central point is associated with the windrow basedon pixel intensity of the windrow pixels. An offset calculator estimatesan offset of the vehicle to the central point of the windrow or a depthaxis to achieve the desired vehicle heading and desired position of thevehicle with respect to the windrow.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of one embodiment of a system for vehicularguidance.

FIG. 2 is a block diagram of another embodiment of a system forvehicular guidance.

FIG. 3 is a flow chart of a first embodiment of a method for guiding avehicle with respect to a windrow or harvested crop row.

FIG. 4 is a flow chart of a second embodiment of a method for guiding avehicle with respect to a windrow or harvested crop row.

FIG. 5A is a raw image with selected training areas.

FIG. 5B is a segmented image that identifies crop pixels or windrowpixels.

FIG. 6 is an illustrative diagram of a search space and an associatedwindrow.

FIG. 7 is an illustrative diagram of a search for a desired headingangle of the vehicle.

FIG. 8 is an illustrative diagram of determining an offset of thevehicle with respect to the desired heading angle.

FIG. 9A is a graph of an illustrative correlation between the searchspace and the windrow versus heading (yaw) of the vehicle.

FIG. 9B through FIG. 9D indicate views of a field from the perspectivelooking toward the direction of travel vehicle that show variousalignments (e.g., and their associated correlations) of the search spacewith respect to the windrow.

FIG. 10 is a diagram that illustrates an imaging coordinate system.

FIG. 11 is a block diagram of yet another embodiment of a system forvehicular guidance.

DESCRIPTION OF THE PREFERRED EMBODIMENT

In accordance with one embodiment of the invention, FIG. 1 shows avehicular guidance system 11 that comprises an imaging device 10 coupledto a data processor 12. The data processor 12 communicates with avehicle guidance controller 22. In turn, the vehicle guidance controller22 directly or indirectly communicates with a steering system 24.

The imaging device 10 is used to collect one or more images from theperspective of a vehicle. The image may contain crop image data,background data, or both. The crop image data may represent a windrow orharvested crop lying on the ground and arranged in one or more rows orgenerally rectangular piles. The data processor 12 may process thecollected images to identify the relative position of a vehicle withrespect to the crop image (e.g., windrow or row of harvested crop lyingon the ground).

The imaging device 10 may comprise a camera using a charged-coupleddevice (CCD), a complementary metal oxide semiconductor (CMOS), oranother sensor that generates color image data, RGB color data, CMYKcolor data, HSV color data, or image data in other color space. RGBcolor data refers to a color model in which red, green and blue light(or signals or data representative thereof) are combined to representother colors. Each pixel or group of pixels of the collected image datamay be associated with an intensity level (e.g., intensity level data)or a corresponding pixel value or aggregate pixel value. In oneembodiment, the intensity level is a measure of the amount of visiblelight energy, infra-red radiation, near-infra-red radiation, ultravioletradiation, or other electromagnetic radiation observed, reflected and/oremitted from one or more objects or any portion of one or more objectswithin a scene or within an image (e.g., a raw or processed image)representing the scene, or portion thereof.

The intensity level may be associated with or derived from one or moreof the following: an intensity level of a red component, greencomponent, or blue component in RGB color space; an intensity level ofmultiple components in RGB color space, a value or brightness in the HSVcolor space; a lightness or luminance in the HSL color space; anintensity, magnitude, or power of observed or reflected light in thegreen visible light spectrum or for another plant color; an intensity,magnitude, or power of observed or reflected light with certain greenhue value or another plant color; and an intensity, magnitude, or powerof observed or reflected light in multiple spectrums (e.g., green lightand infra-red or near infra-red light). For RGB color data, each pixelmay be represented by independent values of red, green and bluecomponents and corresponding intensity level data. CMYK color data mixescyan, magenta, yellow and black (or signals or data representativethereof) to subtractively form other colors. HSV (hue, saturation,value) color data defines color space in terms of the hue (e.g., colortype), saturation (e.g., vibrancy or purity of color), and value (e.g.;brightness of the color). For HSV color data, the value or brightness ofthe color may represent the intensity level. HSL color data definescolor space in terms of the hue, saturation, and luminance (e.g.,lightness). Lightness or luminance may cover the entire range betweenblack to white for HSL color data. The intensity level may be associatedwith a particular color, such as green, or a particular shade or huewithin the visible light spectrum associated with green, or othervisible colors, infra-red radiation, near-infra-red radiation, orultraviolet radiation associated with plant life.

Although other imaging devices may be used, one illustrative example ofan imaging device 10 is a SONY DFW-X710 camera (SONY Electronics Inc.,Park Ridge, N.J.). The imaging device 10 may be associated with atransverse axis and may be associated with scan lines of the image datathat extend generally perpendicular to the transverse axis.

The scan lines or scan line segments represent a group of generallyparallel line segments which may extend into the depth (or depth axis)of the world coordinate system with respect to the imaging device 10.Each scan line segment is separated from an adjacent scan line segmentby a spatial separation (e.g., predetermined spatial separation). In oneembodiment, the scan line segments are generally bounded by a rectanglesearch region (e.g., which may be defined in terms of Xmin, Xmax, Ymin,Ymax); the length of scan line segment is limited by the depth dimension(Ymin, Ymax) in the world space. Further, in one embodiment each scanline may be represented as a two-dimensional array of pixel values orintensity levels.

The scan lines are not transmitted by the imaging device 10, but arereceived by the imaging device 10 within at least one of the visiblelight spectrum, the infra-red light spectrum, the near infra-red lightspectrum, and the ultraviolet light spectrum. If the imaging device 10collects data over both the visible light spectrum and the infra-redspectrum, it is possible to assess the crop color in greater detail thanwith visible light alone. Although the maximum number of scan lines maybe determined based on the maximum resolution of the imaging device 10,in one illustrative configuration, the number of scan lines may bereduced from the maximum number available to reduce the processingresources or computational resources required by the data processor 12.

In one embodiment, the data processor 12 comprises a discriminator 19, adefiner 14, a spatial correlation evaluator 16, an alignment detector 18(e.g., search engine), and an offset calculator 21. The discriminator 19facilitates distinguishing harvested crop image data (e.g., windrowimage data or windrow pixels) from background image data (e.g.,background pixels). The windrows may comprise any harvested crop (e.g.,hay straw or forage), plant clippings, rakings, or vegetative materialsthat are arranged in rows or piles on the ground, for example. Thebackground image data may comprise image data or pixels associated withthe unharvested remainder of the harvested crop and other backgroundmatter distinct from the unharvested remainder. The unharvestedremainder may be referred to as crop stubble, mowed vegetation withintact roots, or vegetation other than the windrows. Other backgroundmatter may comprise the ground, soil, the sky or horizon, buildings,vehicles, among other possibilities. The discriminator 19 facilitatesdistinguishing a primary color (e.g., drying or harvested plant color)of the windrow image data (e.g., windrows pixels) with respect to asecondary color or colors (e.g. growing plant color) of the backgroundimage data. The discriminator 18 may assign a discrimination value orstate value to each pixel of the image data, a corresponding bit map, oranother data representation. Each discrimination value or state valueindicates whether a bit is crop image data or not, or a probabilityindicative of whether or not a bit is windrow image data or a windowpixel.

The definer 14 may define the orientation and configuration of scanlines with respect to the vehicle. The definer 14 may relate the imagingcoordinates of the scene with the vehicular coordinates of scene or realworld coordinates. If the imaging device 10 is mounted in a fixedposition with respect to the vehicle, the vehicular coordinates of thescene and the imaging device 10 may be related by a translation and/orrotation in two or three dimensions.

An spatial correlation evaluator 16 (e.g., intensity evaluator)determines the intensity level of various points that lie on or alongthe scan lines within the search space (e.g., generally rectangularsearch space) in the collected image data. The intensity level may beindicated by the value of pixels or voxels associated with crop imagedata in HSV color space, by the green intensity level of pixels orvoxels in RGB color space, or by another measure of pixel intensity.

For each scan line segment in the search space of the image space, anaverage value or mean value of scan line intensity (or pixel values) maybe used as the intensity level (or pixel level). The spatial correlationevaluator 16 determines an average value or mean value for the intensitylevel or pixel level by summing substantially all (or most) of the pixelvalues (e.g., derived from intensity level) that are on the scan linesegment within the search space and dividing the sum by the number ofpixels associated with a scan line segment within the search space, oran estimate thereof. The scan line segment may have two or more statesor values for each pixel (e.g., sufficient intensity level versusinsufficient intensity level). Because of the perspective view of thescan lines, the fixed length (which may be expressed as the differencebetween Ymax and Ymin), when projected to the image space no longerappears to be fixed. Accordingly, the mean or average value of the scanline intensity level or pixel level represents an objective score of ascan line intensity in image space that is not affected by any potentialperceived change in the fixed length of the scan lines from aperspective view.

The alignment detector 18 determines whether or not the vehicle headingis aligned with the windrows or one or more harvested crop rows lying onthe ground. The scan line segments may be virtually projected onto orinto the search space of image space based on hypothesized attitude(e.g., yaw, pitch, and roll angles) in one or more dimensions within asearch space to determine a preferential attitude in (e.g., yaw, pitch,and roll angle) in one or more dimensions that indicates substantialalignment of the scan lines with one or more windrows, harvested rows ofcrop, generally rectangular piles of hay, straw, forage, clippings, orother plant materials, for example. In one example, the alignmentdetector 18 comprises a search engine for searching the intensity leveldata for a sufficiently high intensity level that meets or exceeds ascan line threshold intensity value with the search space andcorresponds to a desired heading of the vehicle. Respective intensitylevel data may be associated with a corresponding scan line or acorresponding segment thereof within the search space. The scan linesmay be identified by scan line identifier or spatial coordinates, eitherin the image space of the imaging device 10 or real world. The intensitylevel data may be defined as an aggregate intensity level associatedwith a corresponding segment of a scan line or the average, mean or modeintensity level of a corresponding segment of a scan line may betracked.

The alignment detector 18 or data processor determines a desiredheading, a vehicular offset, or both for the vehicle. The desiredheading angle is the angle between the vehicle centerline and thedesired path. The desired path may be associated with a tire, wheel, ortrack of the vehicle traveling in or over the ground or area betweenadjacent harvested crop rows or windrows and generally parallel to thecrop rows or windrows. The vehicular offset refers to the displacementor distance the vehicle is off from the desired path of a vehicle. Forinstance, the vehicular offset may refer to the displacement of areference point on the vehicle (e.g., vehicle COG (center of gravity))with respect to the desired path. The vehicular offset of the vehiclewith respect to the crop image data is generally much smaller than thelateral view range of the imaging device 10.

In one configuration, the alignment detector 18 determines apreferential heading angle for a time interval and the offset calculator21 determines a corresponding vehicular offset for time interval or agenerally overlapping time interval. The vision guidance system 11 maybe used to infer the relative position of the vehicle with respect to acrop feature, such as harvested crop rows, or one or more windrows. Inaddition, the vision guidance system 11 may be configured to detect theend of the row of harvested materials or vegetation clippings, to detectobstacles, or to detect weed infested areas.

The vehicle guidance controller 22 may determine guidance parameters forthe vehicle based on the preferential heading, the vehicular offset, orboth. The guidance parameters may comprise control signals, errorsignals, control data, error data messages, or the like that containinformation on the preferential heading angle and vehicular offset tocontrol steering. For example, the control signals may comprise asteering control signal or data message that is time dependent anddefines a steering angle of the steering shaft.

The vehicle guidance controller 22 uses the guidance parameters ordirectrix to control the steering system 24. For example, the guidanceparameters may direct the vehicle generally parallel to harvested croprows, one or more windrows, or another crop feature. The work vehiclemay comprise a baler, a round baler, a square baler, a wire baler, amowing unit, mower, a raker, a pick-up unit, a cutter, a forageharvester, a tractor (e.g., with or without an implement or attachment),or other agricultural or lawn and garden equipment. A baler may be usedto collect cuttings of crop (e.g., hay, stray or forage) and package orbind the bales with twine, cord, wire, or another binding means.

The steering system 24 may comprise an electrically controlled hydraulicsteering system, an electrically driven rack-and-pinion steering, anAckerman steering system, or another steering system. The steeringsystem 24 may actuate an electro-hydraulic (E/H) unit or anotheractuator to turn one or more wheels.

The vehicular guidance system 111 of FIG. 2 is similar to the vehicularguidance system 11 of FIG. 1, except the vehicular guidance system 111of FIG. 2 further comprises an image segmenter 20 and a reliabilityestimator 23. The discriminator 19 may cooperate with the imagesegmenter 20 to produce a segmented image. The image segmenter 20 mayremove or filter information from the color collected image data toproduce a grey-scale, mono-chrome, color, HSV or other segmented imagedata that excludes background data or all scene data outside of thewindrows or harvested crop image data. For example, the segmented imagedata may be expressed as binary image data, where a pixel value may haveone of two states (e.g., sufficient intensity value or insufficientintensity value).

The reliability estimator 23 estimates a reliability of the preferentialvehicle heading based on compliance with a correlation value associatedwith the alignment of the search space and the windrow or windrowpixels. The reliability estimator 23 may evaluate the intensity levelcriteria associated with one or more windrows of the crop image data.The reliability estimator 23 may use one or more of the followingfactors to determine whether or not a preferential heading derived fromvision data is sufficiently reliable for guidance of a machine during atime interval or period: (1) whether the intensity value (e.g., a firstintensity value) of a first windrow differs from the intensity value(e.g., a second intensity value) of a second windrow by more than aminimum threshold; (2) whether the spacing between a first windrow and asecond windrow falls within a defined row width range; (3) whether amaximum intensity value of the first windrow and the second windrow isgreater than a certain threshold value; and (4) whether the intensityvalue (e.g., designated a first intensity value) of a first windrow andthe intensity value (e.g., second intensity value) of a second windroware individually or collectively less than a corresponding predeterminedthreshold. The reliability estimator 23 may use one or more of thefollowing factors to determine whether or not a vehicular offset derivedfrom vision data is sufficiently reliable for guidance of a machineduring a time interval or period: (1) whether the determined offset isless than or equal to a maximum offset value; and (2) whether thedetermined offset is less than or equal to a spacing between adjacentwindrows.

FIG. 3 shows a method for guiding a vehicle using crop image data (e.g.,crop rows). The method of FIG. 3 begins in step S300.

In step S300, a discriminator 19 or data processor (12 or 112)identifies windrow pixels or windrow image data within a collectedimage. For example, the discriminator 19 may apply a discriminationfunction to identify windrow pixels and background pixels or todistinguish windrow pixels from background pixels. Windrow means a rowor generally linear pile of hay, grain or another crop raked up orotherwise arranged on the ground for drying or otherwise. A windrow maybe gathered as part of the bailing process to create bales of hay oranother crop.

The windrow image data may be characterized by its dried vegetationcolor content, whereas the harvested crop image data (e.g., crop stubbleor recovering crop) may be characterized by other distinctive colorcontent (e.g., which may vary with rainfall, soil moisture,precipitation, temperature, humidity, cumulative sunlight or otherenvironmental factors and the genetic constitution of the particularcrop).

In one example, the discriminator 19 may apply a Linear DiscriminantAnalysis (LDA) method to develop a classification model to segmentharvested crop image data (e.g., hay or windrow) from background imagedata. LDA is a data classification algorithm which maximizes the ratioof between-class variance to within-class variance. The classes may bedefined such that a first class represents windrow image data and asecond class represents background image data. The Linear DiscriminantAnalysis or variation thereof may be applied as follows. The Red, Green,and Blue values of a pixel form a data set X (RGB vector). Duringtraining, regions of crop image data (e.g., the first class) andbackground image data (e.g., the second class) may be manually selectedas training inputs for the LDA algorithm to establish reference cropimage data and reference background image data. Coefficients ofDiscriminant Function (DFC) C_(j) and Co_(j)(j=0 indicating crop imagedata, or 1 indicating background image data) were derived through thefollowing steps:

-   -   (1) Calculating the mean of the vector for each class,        index_mean_(j),    -   (2) Calculating the covariance matrix for each class, cov_(j),    -   (3) Calculating the average of the covariance matrix, cov_mean,        for all classes, and    -   (4) Deriving the DFC:        C _(j)=index_mean_(j)×(cov_mean)⁻¹;  (1)        Co _(j)=−0.5×C _(j)×index_mean_(j)  (2)    -   After the DFC's are acquired using the training data, the DFC's        can be used to calculate the discriminant functions (DF) for        each evaluated pixel:        DF _(j) =C _(j) ×X+Co _(j)  (3)    -   where X is the evaluated pixel's RGB vector.

In one embodiment, a pixel is classified into the class that had thelargest value of DF.

FIG. 5A shows the collected raw image with selected training areas fordefining reference harvested image data and reference background imagedata. The image characteristics of the reference harvested image dataand the reference background image data are stored for future referenceand comparisons to determine whether a portion of a collected image orcollected pixels represents harvested image data (e.g., windrow pixels)or background image data (e.g., background pixels).

FIG. 5B shows a segmented image that uses the LDA algorithm or anotherdata classification scheme. After image segmentation, the crop imagepixels may be set to a first value (e.g., one) and background imagepixels may be set to a second value (e.g., zero) distinct from the firstvalue.

In step S302, the definer 14 or data processor (12 or 112) determines asearch space (e.g., a generally polygonal search space) with respect toa vehicle, where the search space contains a series of scan linesegments. FIG. 6 provides a graphical illustration of a search space600, which may be defined in a first dimension (e.g., transverse orlongitudinal direction) from −X_(max) to X_(max) and in a seconddimension (e.g., depth or latitudinal direction) from Y_(min) toY_(max). The search space 600 overlies a windrow 602, which is exposedabove and below the search space 600. Although the windrow may have anywidth and is not necessarily aligned with the search space, asillustrated in FIG. 6, the windrow is a rectangular area with a width ofX_(max)−(−X_(max))=2 X_(max)=W.

In step S302, a definer 14 or data processor 12 defines a series of scanline segments within the search space that one generally perpendicularto a transverse axis of the vehicle or to an imaging transverse axis ofan imaging device 10. For example, in the image space the scan linesproject out from the front of the vehicle in a direction of traveltoward the windrows. The definer 14 or data processor 12 may align animaging coordinate system of the scene with a vehicle coordinate systemof the scene.

In step S303, the data processor (12 or 112) or evaluator 16 determinesrespective spatial correlations between the defined search space and thewindrow (or windrow pixels) for different angular displacements (e.g.,by scanning or rotation of the search space.) FIG. 7 illustrates theangle heading search. Like reference numbers in FIG. 6 and FIG. 7indicate like elements. FIG. 7 shows the heading search procedure may becarried out by rotating the search space 600 and the world-basedcoordinate system along the origin from −θ_(max) to θ_(max) with anangle resolution (step) of Δθ or otherwise. When the search space 600overlaps with the windrow 602 or windrow pixels, the intersection area(correlation) between the search space and windrow will reach a maximum.The rotational angle at this position of maximum intersection or overlaprepresents the vehicle's desired heading angle (θ) to track the windrow.The spatial correlations generally vary when heading angles are atdifferent positions indicated in FIG. 7.

In step S304, the data processor (12 or 112) or alignment detector 18determines a desired vehicular heading angle as a preferential angulardisplacement associated with a generally maximum spatial correlationbetween the defined search space and the windrow pixels. The maximumspatial correlation is where the polygonal search space overlaps withthe identified windrow pixels to maximum extent. The amount of overlapmay be determined in accordance with various techniques that may beapplied alternately or cumulatively.

Under a first technique, if the respective scan line intensities withinthe search space meet or exceed a scan line threshold intensity value,the maximum spatial correlation between the define search space and thewindrow pixels is established. The scan line threshold intensity valuemay be determined based on empirical studies, field tests, or otherwiseas an indicator of maximum correlation between the search space and thewindrow pixels. The respective scan line intensities within the searchspace may be determined based on one or more of the following: (a) anaverage or mean scan line intensity for scan line segments within thesearch space, (b) a mode scan line intensity for scan line segmentswithin the search space, and (c) an aggregate scan line intensity forscan line segments within the search space.

Under a second technique, the maximum spatial correlation between thesearch space and the windrow pixels is established by applying Green'stheorem, Stoke's theorem, differential geometry, or other applicationsof multidimensional integrals or calculus to evaluate the extent ofoverlap between the search space area (bounded by a first boundary) anda windrow area (bounded by a second boundary).

In step S306, the data processor (12 or 112) or offset calculator 21estimates a central point (e.g., center) associated with a windrow. Inone example, the data processor can estimate a central point of thewindrow as a weighted average of windrow pixel intensity covered by oneor more scan lines. In one implementation of the foregoing weightedaverage of the windrow pixel intensity, higher weights are assigned towindrow pixels that are bounded by other adjacent windrow pixels or thathave a certain minimum density or population of windrow pixels in agiven local region of the image data; lower weights are assigned towindrow pixels that are not bounded by other adjacent windrow pixels orthat have less than a minimum density of windrow pixels in a given localregion of the image data. In FIG. 8, the central point 802 of thewindrow is indicated by centerpoint C_(m) associated with a central axisC_(b)C_(t). The search space axis (indicated by S_(b)S_(t)) may becoextensive with a scan line segment.

In step S308, the data processor (12 or 112) estimates an offset (e.g.,transverse offset) of the vehicle to the central point of the windrow ora depth axis 801 to achieve the desired vehicle heading and desiredposition of the vehicle with respect to the windrow. In one embodiment,the offset may be configured to align or register a scan line segmentwith a central point (e.g., central axis) of the windrow. When thedesired heading angle is found, the central point (e.g., centerpointC_(m) or windrow central axis (C_(b)C_(t))) of the windrow can becalculated by weighted average of pixel intensities covered by the scanlines. The corrective distance (d′) between the search space axis(S_(b)S_(t)) and central axis (C_(b)C_(t)) is the lateral displacementof windrow from the desired position, whereas the offset (d) may bemeasured with respect to a depth axis 801.

A control point is defined as the point to determine the offset of thetracking or steering of the vehicle from a desired position and desiredheading. The control point is associated with a central point of thewindrow or any point along the central axis C_(b)C_(t), or an extensionthereof. The offset (d) at the control point that is L long from theorigin ◯ along the central axis C_(b)C_(t) may be determined by thefollowing equation(s):d=L*sin(θ)−d′/cos(θ) or d=L*sin(θ)d′/cos(θ),where L is a scan line axis, d is the offset or transverse distancebetween the scan line axis and a depth axis, θ is an angle between thedepth axis and the scan line axis, and d′ is the distance between centerof the search space axis (S_(b)S_(t)) line and central axis C_(b)C_(t)at the control point (e.g., associated with S_(m) and/or C_(m)). Theoffset d varies with the displacement between the actual position andthe desired position with the desired heading angle. By convention, d ispositive if the windrow is oriented to the right of the search spacewith respect to the direction of travel and d is negative if the windrowis oriented to the left of the search space.

When L=0, the control point is very close to origin (0) of thecoordinate system and coextensive with the position of the imagingsystem, the offset d is solely determined by d′ and heading angle θ.When the offset is 0 at the imaging device's position, the frontwheel(s) of the vehicle usually may crush or run over the windrow orharvested row of crop. Therefore, control point at L=0 (close to origin)may not guide the vehicle as effectively as a control point at L>0 does.Accordingly, an alternative control point of L>0 is preferably selectedbased the geometry of the work vehicle and the position of the imagingsystem on the work vehicle. The alternative control point is selectedsuch that the front wheel(s) will not travel on the windrow or harvestedcrop. In an alternate embodiment, the control point can also be selectedto be in the center (C_(m), FIG. 8) of a defined scan line segment or tobe near a bottom (C_(b), FIG. 8) of the scan line segments. Accordingly,the imaging system looks ahead a farther distance as guidance goal,which may be regarded as somewhat equivalent to changing the gain ofvehicular guidance controller 22 (e.g., steering controller).

FIG. 7 illustrates vision directrix calculations of a vision guidancesystem that may be used to correct or modify the receiver directrixcalculations of a location-determining receiver (e.g., GlobalPositioning System Receiver with differential correction) associatedwith the vehicle. If the control point is at C_(m), thereceiver-determined offset (e.g., GPS-based offset) is generally equalto vision offset of the vision guidance system. The receiver offset isindependent of control point, whereas the vision offset will depend onwhere the control point is.

The method of FIG. 4 is similar to the method of FIG. 3, except stepsS303 and S304 are replaced with step S403 and S404, respectively. Likereference numbers indicate like steps or procedures in FIG. 3 and FIG.4.

Step S403 may follow step S302. In step S403, a data processor (12 or122) or evaluator 16 determines respective scan line intensitiesassociated with the scan line segments for different angulardisplacements (e.g., by scanning or rotation) of the search space. Forexample, the spatial correlation evaluator 16 may determine scan lineintensity data for each of the scan line segments within the searchspace with respect to corresponding harvested crop image data (e.g.,windrows). Step S403 may be carried out in accordance with varioustechniques that may be applied independently or cumulatively.

Under a first technique, the spatial correlation evaluator 16 determinesan average or mean scan line intensity for a corresponding scan linesegment associated with the collected image data for a correspondingangular displacement, among different angular displacements. The spatialcorrelation evaluator 16 may determine the average or mean scan lineintensity for each scan line segment within the search space, or aportion thereof, for a corresponding angular displacement, amongdifferent angular displacements.

Under a second technique, the spatial correlation evaluator 16determines a mode scan line intensity for a corresponding scan linesegment for a corresponding angular displacement, among differentangular displacements. The spatial correlation evaluator 16 maydetermine the mode scan line intensity for each scan line (or segment)within the search space, or a portion thereof, for a correspondingangular displacement, among different angular displacements.

Under a third example, the spatial correlation evaluator 16 determinesan aggregate scan line intensity for a corresponding segment of a scanline for a corresponding angular displacement, among different angulardisplacements. Here, the spatial correlation evaluator 16 may determinea sum of the intensity values associated with each pixel or voxel alonga scan line or generally intercepting it within the search space, or aportion thereof.

In step S404, a data processor (12 or 112) or alignment detector 18determines a desired vehicular heading as a preferential angulardisplacement associated with a scan line intensity meeting or exceedinga threshold scan line intensity value. For example, an alignmentdetector 18 (e.g., search engine) identifies a desired heading of thevehicle that is generally aligned with respect to windrows (e.g., orharvested rows of crop, associated with the crop image data, based onthe determined scan line intensity meeting or exceeding a threshold scanline intensity value). The threshold scan line intensity value maycomprise a reference value that is associated with a first reliabilitylevel (e.g., 99 percent reliability or probability) of identifying amaximum intensity level (or sufficiently high intensity level indicativeof substantial alignment with a crop feature) of a scan line or group ofpixels in the image data under defined ambient light conditions. Thethreshold scan line intensity value may be determined by empiricalstudies, trial-and-error, field tests, or otherwise, and may vary withthe type of vegetation (e.g., corn versus soybeans), vegetation statusor health (e.g., plant tissue nitrogen level) and the ambient lightingconditions, for instance. In one example, the desired heading of thevehicle is consistent with tracking one or more windrows. In anotherexample, the desired path of travel of the vehicle is selected toefficiently gather or collect the harvested crop, or otherwise. If thevehicle is in a generally aligned state with respect to the harvestedcrop or one or more windrows, a vehicle may track a desired path that isgenerally parallel to the harvested crop or one or more windrows.

FIG. 9A is a graph of correlation between the search space and thewindrow versus heading (yaw) of the vehicle. The horizontal axisrepresents the heading (yaw, which may be expressed in degrees) of thevehicle, whereas the vertical axis represents correlation (e.g.,normalized correlation) or an analogous correlation score between thesearch space alignment and the windrow.

FIG. 9B through FIG. 9C indicate image views of a field from theperspective of the direction of travel vehicle that show variousalignments (e.g., and their associated correlations) of the search space912 with respect to the windrow 908. Each image of FIG. 9B through FIG.9D includes a windrow 908, background 910, and a search space 912.

In FIG. 9B, the search space 912 is positioned to the right of thewindrow 908, and the heading (yaw) of the vehicle is approximately −10degrees with a corresponding correlation (e.g., score) of less than0.25. The alignment of FIG. 9B corresponds to point 902 in the graph ofFIG. 9A.

In FIG. 9C, the search space 912 is aligned with respect to the windrow,and the heading of the vehicle is approximately 0 degrees with acorresponding correlation of approximately one. The alignment of FIG. 9Cis representative of a desired heading and is reflected in point 904 ofFIG. 9A.

In FIG. 9D, the search space is positioned to the left of the windrow,and the heading (yaw) of the vehicle is approximately 5 degrees with acorresponding correlation (e.g., score) of less than 0.5. The alignmentof FIG. 9D corresponds to point 906 in the graph of FIG. 9D.

As shown in FIG. 10, the world coordinate system may be defined withrespect to the vehicle coordinate system. In the vehicle coordinatesystem, the vehicle forward direction is regarded as the Y direction,the vehicle lateral direction is designated X, and vertical direction isdesignated Z. X is also referred to as the transverse axis of thevehicle. The vehicle coordinates in FIG. 10 are the equivalent of (XYplane) world coordinates.

The imaging coordinate system (e.g., X_(c)Y_(c)Z_(c) in FIG. 10) is animaging device centered coordinate system. The X_(c)-axis defines anaxis that extends laterally (e.g., linearly from a left-to-rightdirection); the Y_(c)-axis defines an axis that extends upward (e.g.,linearly from a low-to-high direction); and the Z_(c)- axis follows theoptical or physical centerline of a lens of the imaging device 10. TheX_(c)-axis is also referred to as the transverse axis of the imagingdevice or imaging system. World coordinate and imaging coordinatesystems both belong to world space. However, an when an object isprojected into the imaging device 10, the formed two dimensional imagedata lies in the image space, as opposed to world space.

The imaging device 10, the definer 14, or the data processor 12 maycalibrate the image data to transform a point's coordinates in worldspace to its corresponding pixel's coordinates in image space (e.g.,image plane). Calibration of the imaging device 10 includes extrinsicparameters and intrinsic parameters. Extrinsic parameters define how totransform an object from world coordinates to imaging coordinates inworld space. Extrinsic parameters include the camera's three dimensionalcoordinates in the world coordinate system and its pitch, roll, and yawangles. Once the installation of the imaging device 10 is fixed,extrinsic parameters do not need to change. Intrinsic parameters definehow to transform an object from world space to image space. Theintrinsic parameters include the camera's focus length, its image centerin the image plane, and related distortion coefficients. The intrinsicparameters are fixed for a given imaging device 10 and lens. Variousalgorithms may be employed to map a point from the world space to apoint in the two dimensional image plane, or vice versa.

In any of the embodiments or methods disclosed herein, the headingsearch may be conducted in world space or in image space. For eachcombination of (yaw, pitch), a mapping table between all the points inthe scan lines in world space and their corresponding pixel coordinatesin image space (e.g. image plane) is established in the algorithm'sinitialization phase. The points in scan lines that are outside of imagewindow will be truncated and marked as not available in the mappingtable. After the mapping is done, it is straightforward to calculate theintensity along a certain scan line or scan lines by finding the valueof the pixels lying on it. If the guidance parameters are calculated inthe image space first, then those guidance parameters are transformedinto the world space.

The guidance system 211 of FIG. 11 is similar to the vision guidancesystem 11 of FIG. 1 or the vision guidance system of FIG. 2, except theguidance system of FIG. 11 further includes location-determiningreceiver 33 and a selector 35. Like reference numbers in FIG. 1, FIG. 2and FIG. 15 indicate like elements.

The location determining receiver 33 may comprise a Global PositioningSystem (GPS) receiver with or without differential correction. Thelocation-determining receiver 33 provides an alternate guidance data orposition data when the imaging device 10 and data processor 12 producegenerally unreliable data during a time period (e.g., an interval). Incontrast, if the location-determining receiver 33 fails or is unreliablebecause of satellite dropouts or unreliable communication between thevehicle and a base station that transmits differential correctioninformation (e.g., operating in the RTK mode), the imaging device 10 mayprovide reliable guidance information, subject to the determination ofthe reliability estimator 23.

The selector 35 may use the reliability estimator 23 to determinewhether to apply image data from the imaging device 10 or position datafrom the location-determining receiver 33 to guide the vehicle inaccordance with a vehicular offset and preferential heading angle. Oncethe vehicle is moved in accordance with the vehicular offset andpreferential heading angle, the captured image data reflect the newvehicle position with respect to crop rows. Thus, the guidance system211 may be operated as a closed-loop control system in which vehiclepath data provides feedback or other reference information to theimaging device 10 and the location determining receiver 33.

Having described the preferred embodiment, it will become apparent thatvarious modifications can be made without departing from the scope ofthe invention as defined in the accompanying claims.

The following is claimed:
 1. A method of guiding a vehicle, the methodcomprising: identifying windrow pixels associated with a windrow withina collected color image; identifying a primary color of the windrowpixels and one or more secondary colors of background pixels; assigninga discrimination value to each pixel in the identified windrow pixels,wherein the discrimination value indicates whether the each pixel iscrop image data or background image data, and wherein each pixel in thecollected color image is associated with an intensity level; defining asearch space with respect to a vehicle, the search space containing aseries of scan line segments; determining respective spatialcorrelations between the defined search space and the windrow pixels fordifferent angular displacements of the search space, wherein thedetermining respective spatial correlations comprises determiningrespective scan line intensities associated with the scan line segmentsfor different angular displacements; determining a desired vehicularheading as a preferential angular displacement associated with agenerally maximum spatial correlation between the defined search spaceand the windrow pixels; estimating a central point associated with thewindrow based on pixel intensity of the windrow pixels; and estimatingan offset of the vehicle to the central point of the windrow or a depthaxis to achieve the desired vehicle heading and desired position of thevehicle with respect to the windrow.
 2. The method according to claim 1wherein the determining of the respective scan line intensitiescomprises determining an average or mean scan line intensity for scanline segments within the search space.
 3. The method according to claim1 wherein the determining of the respective scan line intensitiescomprises determining a mode scan line intensity for scan line segmentswithin the search space.
 4. The method according to claim 1 wherein thedetermining of the respective scan line intensities comprisesdetermining an aggregate scan line intensity for scan line segmentswithin the search space.
 5. The method according to claim 1 wherein thedetermining a desired vehicular heading comprises determining thedesired vehicular heading as a preferential angular displacementassociated with a scan line intensity within the search space meeting orexceeding a threshold scan line intensity value.
 6. The method accordingto claim 1 further comprising: deriving a segmented image of crop rowsas the crop image data from the collected color image.
 7. The methodaccording to claim 1, further comprising: estimating a reliability ofthe preferential angular heading based on compliance with a correlationvalue associated with the alignment of the search space and the windrowpixels, wherein the intensity level associated with one or more windrowsis evaluated based on the intensity level associated with the each pixelin the collected color image.
 8. The method according to claim 1,wherein the windrow pixels are identified by: calculating the mean ofthe vector for each class in a number of classes, index_mean_(j),wherein the number of classes include a first class representing windrowpixels and a second class representing background pixels; calculatingthe covariance matrix for each class, cov_(j); calculating the averageof the covariance matrix, cov_mean, for all classes; and deriving thecoefficients of discriminant function:C _(j)=index_mean_(j)×(cov_mean)⁻¹;Co _(j)=−0.5×C _(j)×index_mean_(j).
 9. The method according to claim 8,wherein responsive to deriving the coefficients of discriminantfunction, the coefficients of discriminant function are used tocalculate the discriminant functions for each pixel evaluated:DF _(j) =C _(j) ×X+Co _(j), wherein X is the vector of the each pixelevaluated.
 10. A method of guiding a vehicle, the system comprising:identifying windrow image data associated with a harvested crop in acollected color image and background image data in the collected colorimage based on color and capable of assigning a discrimination value toeach portion of image data identified in the collected color image basedon whether the each portion is window image data or background imagedata; defining a search space with respect to a vehicle, the searchspace containing a series of scan line segments; determining respectivespatial correlations between the define defined search space and thewindrow image data for different angular displacements of the searchspace, wherein the respective spatial correlations are based on scanline intensities associated with the scan line segments for differentangular displacements; determining a desired vehicle heading as apreferential angular heading associated with a generally maximum spatialcorrelation between the defined search space and the windrow image data;and identifying a desired offset of the vehicle that is generallyaligned with respect to the windrow, associated with the collected colorimage, consistent with the desired vehicle heading.
 11. The method ofclaim 10, wherein the each portion of image data identified is selectedfrom one of pixels and bit maps.